def create_base_constituents():
"""
Builds the raw skeleton of a constituent from constituents.csv
"""
reader = Converter.process_csv(os.environ['BASEPATH'] + 'constituents.csv')
constituents = {}
for row in reader:
row = Converter.remove_bom(row)
if (row['AlphaSort'] == None):
print "No AlphaSort in: " + row['ConstituentID']
row['AlphaSort'] = ''
row['nameSort'] = row['AlphaSort'].split(" ")[0]
row['id'] = row['ConstituentID']
constituents[row['ConstituentID']] = row
return constituents
def create_base_constituents():
"""
Builds the raw skeleton of a constituent from constituents.csv
"""
reader = Converter.process_csv(os.environ['BASEPATH'] + 'constituents.csv')
constituents = {}
for row in reader:
row = Converter.remove_bom(row)
if (row['AlphaSort'] == None):
print "No AlphaSort in: " + row['ConstituentID']
row['AlphaSort'] = ''
row['nameSort'] = row['AlphaSort'].split(" ")[0]
row['id'] = row['ConstituentID']
constituents[row['ConstituentID']] = row
return constituents
def get_join_data(filepath):
"""
To denormalize CSV data term IDs
"""
reader = Converter.process_csv(filepath)
data = {}
for row in reader:
row = Converter.remove_bom(row)
tmp = []
for k in row:
tmp.append(row[k])
if tmp[1].isdigit():
data[tmp[1]] = tmp[0]
else:
data[tmp[0]] = tmp[1]
return data
def get_join_data(filepath):
"""
To denormalize CSV data term IDs
"""
reader = Converter.process_csv(filepath)
data = {}
for row in reader:
row = Converter.remove_bom(row)
tmp = []
for k in row:
tmp.append(row[k])
if tmp[1].isdigit():
data[tmp[1]] = tmp[0]
else:
data[tmp[0]] = tmp[1]
return data
def process_constituents(endpoint):
"""
Consolidates all constituent data into a single dictionary. Pushes each dictionary as a document to Elastic.
"""
start = timeit.default_timer()
constituents = create_base_constituents()
counter = 0
tables = ["format","biography","address","gender","process","role","collection"]
joins = ["formats","biographies",["addresstypes","countries"],"genders","processes","roles","collections"]
location_pattern = re.compile("(\-?\d+(\.\d+)?)\s*,\s*(\-?\d+(\.\d+)?)")
for table in tables:
reader = Converter.process_csv(os.environ['BASEPATH'] + table + ".csv")
if type(joins[counter]) is str:
joindata = get_join_data(os.environ['BASEPATH'] + joins[counter] + ".csv")
else:
j0 = get_join_data(os.environ['BASEPATH'] + joins[counter][0] + ".csv")
j1 = get_join_data(os.environ['BASEPATH'] + joins[counter][1] + ".csv")
joindata = [j0,j1]
for row in reader:
row = Converter.remove_bom(row)
if ((row['ConstituentID'] in constituents) == False):
print "No constituent:" + row['ConstituentID']
continue
if ((table in constituents[row['ConstituentID']]) == False):
constituents[row['ConstituentID']][table] = []
cid = row['ConstituentID']
if not 'ConAddressID' in row:
del row['ConstituentID']
else:
row['id'] = row['ConAddressID']
# add the value of the term id
if 'TermID' in row:
if ((row['TermID'] in joindata) == False):
print "No " + table + ":" + row['TermID']
else:
row['Term'] = joindata[row['TermID']]
if 'AddressTypeID' in row:
if ((row['AddressTypeID'] in joindata[0]) == False):
print "No " + joins[counter][0] + ":" + row['AddressTypeID']
print joindata[0]
else:
row['AddressType'] = joindata[0][row['AddressTypeID']]
if ((row['CountryID'] in joindata[1]) == False):
print "No " + joins[counter][1] + ":" + row['CountryID']
else:
row['Country'] = joindata[1][row['CountryID']]
if 'Remarks' in row:
row['Remarks'] = Converter.convert_whitespace(row['Remarks'])
if location_pattern.match(row['Remarks']):
latlon = Converter.compress_address(row['Remarks'])
row['Remarks'] = ",".join(latlon)
row['Location'] = { "lat" : float(latlon[0]), "lon" : float(latlon[1]) }
constituents[cid][table].append(row)
counter = counter + 1
end = timeit.default_timer()
print "\n\nProcessed CSVs in " + str(end - start) + " seconds\n"
print "\n\nPreparing indexing actions...\n"
start = timeit.default_timer()
# now on to elastic (index already created)
actions = []
addresslist = []
for index, cid in enumerate(constituents):
addresses = []
if 'address' in constituents[cid]:
# sort addresses
addresses = Converter.sort_addresses(constituents[cid]['address'])
constituents[cid]['addressTotal'] = len(constituents[cid]['address'])
del constituents[cid]['address']
# put the addresses
addresslist = addresslist + addresses
# put the constituent in there
actions.append(create_constituent(constituents[cid]))
end = timeit.default_timer()
print "\n\nActions prepared in " + str((end - start)/60) + " minutes\n"
print "\n\nIndexing...\n"
create_indices(endpoint)
es = Elasticsearch([endpoint], timeout=360, max_retries=10, retry_on_timeout=True)
# split the actions into batches of 10k
print " Constituents..."
index = 0
n = 10000
splitactions = split_list(actions, n)
for actionchunk in splitactions:
print " actions " + str(index*n) + " to " + str((index+1)*n)
index = index + 1
helpers.bulk(es, actionchunk)
print " Addesses..."
index = 0
splitaddresses = split_list(addresslist, n)
for addresschunk in splitaddresses:
print " actions " + str(index*n) + " to " + str((index+1)*n)
index = index + 1
helpers.bulk(es, [build_action(value, 'pic', 'address') for value in addresschunk])
return constituents
def process_constituents(endpoint):
"""
Consolidates all constituent data into a single dictionary. Pushes each dictionary as a document to Elastic.
"""
start = timeit.default_timer()
constituents = create_base_constituents()
counter = 0
tables = [
"format", "biography", "address", "gender", "process", "role",
"collection"
]
joins = [
"formats", "biographies", ["addresstypes", "countries"], "genders",
"processes", "roles", "collections"
]
location_pattern = re.compile("(\-?\d+(\.\d+)?)\s*,\s*(\-?\d+(\.\d+)?)")
for table in tables:
reader = Converter.process_csv(os.environ['BASEPATH'] + table + ".csv")
if type(joins[counter]) is str:
joindata = get_join_data(os.environ['BASEPATH'] + joins[counter] +
".csv")
else:
j0 = get_join_data(os.environ['BASEPATH'] + joins[counter][0] +
".csv")
j1 = get_join_data(os.environ['BASEPATH'] + joins[counter][1] +
".csv")
joindata = [j0, j1]
for row in reader:
row = Converter.remove_bom(row)
if ((row['ConstituentID'] in constituents) == False):
print "No constituent:" + row['ConstituentID']
continue
if ((table in constituents[row['ConstituentID']]) == False):
constituents[row['ConstituentID']][table] = []
cid = row['ConstituentID']
if not 'ConAddressID' in row:
del row['ConstituentID']
else:
row['id'] = row['ConAddressID']
# add the value of the term id
if 'TermID' in row:
if ((row['TermID'] in joindata) == False):
print "No " + table + ":" + row['TermID']
else:
row['Term'] = joindata[row['TermID']]
if 'AddressTypeID' in row:
if ((row['AddressTypeID'] in joindata[0]) == False):
print "No " + joins[counter][0] + ":" + row['AddressTypeID']
print joindata[0]
else:
row['AddressType'] = joindata[0][row['AddressTypeID']]
if ((row['CountryID'] in joindata[1]) == False):
print "No " + joins[counter][1] + ":" + row['CountryID']
else:
row['Country'] = joindata[1][row['CountryID']]
if 'Remarks' in row:
row['Remarks'] = Converter.convert_whitespace(row['Remarks'])
if location_pattern.match(row['Remarks']):
latlon = Converter.compress_address(row['Remarks'])
row['Remarks'] = ",".join(latlon)
row['Location'] = {
"lat": float(latlon[0]),
"lon": float(latlon[1])
}
constituents[cid][table].append(row)
counter = counter + 1
end = timeit.default_timer()
print "\n\nProcessed CSVs in " + str(end - start) + " seconds\n"
print "\n\nPreparing indexing actions...\n"
start = timeit.default_timer()
# now on to elastic (index already created)
actions = []
addresslist = []
for index, cid in enumerate(constituents):
addresses = []
if 'address' in constituents[cid]:
# sort addresses
addresses = Converter.sort_addresses(constituents[cid]['address'])
constituents[cid]['addressTotal'] = len(
constituents[cid]['address'])
del constituents[cid]['address']
# put the addresses
addresslist = addresslist + addresses
# put the constituent in there
actions.append(create_constituent(constituents[cid]))
end = timeit.default_timer()
print "\n\nActions prepared in " + str((end - start) / 60) + " minutes\n"
print "\n\nIndexing...\n"
create_indices(endpoint)
es = Elasticsearch([endpoint],
timeout=360,
max_retries=10,
retry_on_timeout=True)
# split the actions into batches of 10k
print " Constituents..."
index = 0
n = 10000
splitactions = split_list(actions, n)
for actionchunk in splitactions:
print " actions " + str(index * n) + " to " + str((index + 1) * n)
index = index + 1
helpers.bulk(es, actionchunk)
print " Addesses..."
index = 0
splitaddresses = split_list(addresslist, n)
for addresschunk in splitaddresses:
print " actions " + str(index * n) + " to " + str((index + 1) * n)
index = index + 1
helpers.bulk(
es,
[build_action(value, 'pic', 'address') for value in addresschunk])
return constituents
